StarGO: A New Method to Identify the Galactic Origins of Halo Stars

We develop a new method StarGO (Stars' Galactic Origin) to identify the galactic origins of halo stars using their kinematics. Our method is based on self-organizing map (SOM), which is one of the most popular unsupervised learning algorithms. StarGO combines SOM with a novel adaptive group identification algorithm with essentially no free parameters. In order to evaluate our model, we build a synthetic stellar halo from mergers of nine satellites in the Milky Way. We construct the mock catalogue by extracting a heliocentric volume of 10 kpc from our simulations and assigning expected observational uncertainties corresponding to bright stars from Gaia DR2 and LAMOST DR5. We compare the results from StarGO against that from a Friends-of-Friends (FoF) based method in the space of orbital energy and angular momentum. We show that StarGO is able to systematically identify more satellites and achieve higher number fraction of identified stars for most of the satellites within the extracted volume. When applied to data from Gaia DR2, StarGO will enable us to reveal the origins of the inner stellar halo in unprecedented detail.Comment: 11 pages, 7 figures, Accepted for publication in Ap

Self-Organizing Maps and Parton Distributions Functions

We present a new method to extract parton distribution functions from high energy experimental data based on a specific type of neural networks, the Self-Organizing Maps. We illustrate the features of our new procedure that are particularly useful for an anaysis directed at extracting generalized parton distributions from data. We show quantitative results of our initial analysis of the parton distribution functions from inclusive deep inelastic scattering.Comment: 8 pages, 4 figures, to appear in the proceedings of "Workshop on Exclusive Reactions at High Momentum Transfer (IV)", Jefferson Lab, May 18th -21st, 201

Self-Organizing Maps Algorithm for Parton Distribution Functions Extraction

We describe a new method to extract parton distribution functions from hard scattering processes based on Self-Organizing Maps. The extension to a larger, and more complex class of soft matrix elements, including generalized parton distributions is also discussed.Comment: 6 pages, 3 figures, to be published in the proceedings of ACAT 2011, 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Researc

A near-IR line of Mn I as a diagnostic tool of the average magnetic energy in the solar photosphere

We report on spectropolarimetric observations of a near-IR line of Mn I located at 15262.702 A whose intensity and polarization profiles are very sensitive to the presence of hyperfine structure. A theoretical investigation of the magnetic sensitivity of this line to the magnetic field uncovers several interesting properties. The most important one is that the presence of strong Paschen-Back perturbations due to the hyperfine structure produces an intensity line profile whose shape changes according to the absolute value of the magnetic field strength. A line ratio technique is developed from the intrinsic variations of the line profile. This line ratio technique is applied to spectropolarimetric observations of the quiet solar photosphere in order to explore the probability distribution function of the magnetic field strength. Particular attention is given to the quietest area of the observed field of view, which was encircled by an enhanced network region. A detailed theoretical investigation shows that the inferred distribution yields information on the average magnetic field strength and the spatial scale at which the magnetic field is organized. A first estimation gives ~250 G for the mean field strength and a tentative value of ~0.45" for the spatial scale at which the observed magnetic field is horizontally organized.Comment: 42 pages, 17 figures, accepted for publication in the Astrophysical Journal. Figures 1 and 9 are in JPG forma

Multivariate Techniques for Identifying Diffractive Interactions at the LHC

31 pages, 14 figures, 11 tablesClose to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.Peer reviewe

Learning a world model and planning with a self-organizing, dynamic neural system

We present a connectionist architecture that can learn a model of the relations between perceptions and actions and use this model for behavior planning. State representations are learned with a growing self-organizing layer which is directly coupled to a perception and a motor layer. Knowledge about possible state transitions is encoded in the lateral connectivity. Motor signals modulate this lateral connectivity and a dynamic field on the layer organizes a planning process. All mechanisms are local and adaptation is based on Hebbian ideas. The model is continuous in the action, perception, and time domain.Comment: 9 pages, see http://www.marc-toussaint.net

Transactions, Transformations, Translations: Metrics that Matter for Building, Scaling, and Funding Social Movements

This report provides an evaluative framework and key milestones to gauge movement building. Aiming to bridge the gap between the field of community organizing that relies on the one-on-one epiphanies of leaders and the growing philanthropic emphasis on evidence-based giving, the report stresses three main insights. The first is that any good set of movement metrics should capture quantity and quality, numbers and nuance, transactions and transformations. They are related -- an energized leader with a clear power analysis (a transformative measure) may turn out more members for a coalition rally (a transactional measure) -- and the report offers a matrix that weaves together both types of metrics across ten different movement-building strategies. The second is that a movement is more than one organization -- and if the whole is to be greater than the sum of its parts, we must measure accordingly. While report includes measures of success at the organizational level, it attempts to move beyond and focus on whether groups can align and work together to create a more powerful force for social change -- suggesting that in the same way that movements need to scale up to face the challenges of our times, metrics, too, must expand to capture the whole. The third is that metrics must be co-created, not imposed. Recognizing the gravity of the times and hoping to gauge their effectiveness, movement builders are eager to come up with a common language and framework for themselves -- and are developing the tools and capacities to do so. The report suggests that the funder-grantee relationship can build on this wisdom in the field and develop a set of evaluative measures that are not onerous requirements but tools for mutual accountability. The report also offers a set of recommendations to funders and the field, ranging from practical steps (like building a new toolbox of measures, improving the capacity to use them, and documenting innovation and experimentation) to more far-reaching suggestions about leadership development, the connection of policy outcomes with broader social change, and the need to generate movement-level measures. We, at USC PERE, hope this report contributes to a conversation about how to best capture transformations as well as transactions in social movement organizing, and how to build the broader public and philanthropic support necessary to realize the promise of a more inclusive America